Using Neural Networks and Immune Algorithms to Find the Optimal Parameters for an IC Wire Bonding Process

摘要

The wire bonding is the key process in an IC chip-package. It is an urgent problem for IC chip-package industry to improve the wire bonding process capability. In this study, an application of artificial neural networks (ANN) and artificial immune systems (AIS) is proposed to optimize parameters for the wire bonding process in order to achieve highly level performance and quality. In this research, the algorithm of AIS with memory cell and suppressor cell mechanisms is developed. A back propagation ANN is used to establish the nonlinear multivariate relationships between the wire boning parameters and responses. Then a Taguchi method is applied to identify the critical parameters of the AIS. Finally, the AIS is applied to find the most desired parameter settings by using the output of ANN as the affinity measure. A comparison between the proposed AIS and a genetic algorithm is conducted in this study. The comparison shows that the searching quality of the proposed AIS is more effective than the GA in finding the optimal wire bonding process parameters.